from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match¶reporting = Reporting("sklearnex", config="config.yml")
reporting.run()
KNeighborsClassifier_brute_force: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.013 | 0.0 | 6.091 | 0.0 | -1 | 100 | NaN | NaN | 0.068 | 0.0 | 0.193 | 0.0 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.013 | 0.0 | 6.022 | 0.0 | 1 | 100 | NaN | NaN | 0.051 | 0.0 | 0.259 | 0.0 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.013 | 0.0 | 6.016 | 0.0 | -1 | 1 | NaN | NaN | 0.051 | 0.0 | 0.259 | 0.0 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.013 | 0.0 | 5.974 | 0.0 | -1 | 5 | NaN | NaN | 0.053 | 0.0 | 0.252 | 0.0 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.013 | 0.0 | 5.953 | 0.0 | 1 | 5 | NaN | NaN | 0.051 | 0.0 | 0.262 | 0.0 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.014 | 0.0 | 5.701 | 0.0 | 1 | 1 | NaN | NaN | 0.051 | 0.0 | 0.276 | 0.0 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.006 | 0.0 | 0.286 | 0.0 | -1 | 100 | NaN | NaN | 0.010 | 0.0 | 0.589 | 0.0 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.006 | 0.0 | 0.274 | 0.0 | 1 | 100 | NaN | NaN | 0.010 | 0.0 | 0.613 | 0.0 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.006 | 0.0 | 0.267 | 0.0 | -1 | 1 | NaN | NaN | 0.009 | 0.0 | 0.638 | 0.0 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.007 | 0.0 | 0.229 | 0.0 | -1 | 5 | NaN | NaN | 0.010 | 0.0 | 0.709 | 0.0 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.006 | 0.0 | 0.266 | 0.0 | 1 | 5 | NaN | NaN | 0.009 | 0.0 | 0.648 | 0.0 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.005 | 0.0 | 0.320 | 0.0 | 1 | 1 | NaN | NaN | 0.009 | 0.0 | 0.531 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 3.174 | 0.081 | 0.000 | 0.003 | -1 | 100 | 0.840 | 0.733 | 0.399 | 0.015 | 7.957 | 0.367 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.029 | 0.002 | 0.000 | 0.029 | -1 | 100 | 1.000 | 1.000 | 0.011 | 0.000 | 2.564 | 0.198 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 2.431 | 0.030 | 0.000 | 0.002 | 1 | 100 | 0.840 | 0.636 | 0.392 | 0.004 | 6.197 | 0.098 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.024 | 0.001 | 0.000 | 0.024 | 1 | 100 | 1.000 | 1.000 | 0.013 | 0.002 | 1.833 | 0.253 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 2.064 | 0.042 | 0.000 | 0.002 | -1 | 1 | 0.632 | 0.840 | 0.445 | 0.007 | 4.641 | 0.119 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.028 | 0.002 | 0.000 | 0.028 | -1 | 1 | 0.000 | 1.000 | 0.011 | 0.000 | 2.453 | 0.223 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 3.103 | 0.043 | 0.000 | 0.003 | -1 | 5 | 0.725 | 0.636 | 0.394 | 0.003 | 7.869 | 0.130 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.028 | 0.002 | 0.000 | 0.028 | -1 | 5 | 1.000 | 1.000 | 0.012 | 0.000 | 2.455 | 0.204 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 2.426 | 0.022 | 0.000 | 0.002 | 1 | 5 | 0.725 | 0.840 | 0.447 | 0.006 | 5.422 | 0.088 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.023 | 0.000 | 0.000 | 0.023 | 1 | 5 | 1.000 | 1.000 | 0.012 | 0.001 | 1.915 | 0.114 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 1.434 | 0.011 | 0.001 | 0.001 | 1 | 1 | 0.632 | 0.733 | 0.401 | 0.007 | 3.577 | 0.067 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.025 | 0.002 | 0.000 | 0.025 | 1 | 1 | 0.000 | 1.000 | 0.012 | 0.001 | 2.090 | 0.262 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 2.674 | 0.059 | 0.000 | 0.003 | -1 | 100 | 0.890 | 0.854 | 0.087 | 0.002 | 30.754 | 1.072 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.011 | 0.002 | 0.000 | 0.011 | -1 | 100 | 1.000 | 0.000 | 0.001 | 0.000 | 17.871 | 4.352 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 2.291 | 0.119 | 0.000 | 0.002 | 1 | 100 | 0.890 | 0.823 | 0.084 | 0.001 | 27.315 | 1.464 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.005 | 0.002 | 0.000 | 0.005 | 1 | 100 | 1.000 | 0.000 | 0.001 | 0.000 | 7.284 | 3.519 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 2.010 | 0.055 | 0.000 | 0.002 | -1 | 1 | 0.844 | 0.876 | 0.139 | 0.004 | 14.492 | 0.611 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.017 | 0.017 | 0.000 | 0.017 | -1 | 1 | 1.000 | 0.000 | 0.001 | 0.000 | 24.152 | 25.050 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 2.919 | 0.170 | 0.000 | 0.003 | -1 | 5 | 0.882 | 0.823 | 0.085 | 0.004 | 34.204 | 2.536 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.010 | 0.005 | 0.000 | 0.010 | -1 | 5 | 1.000 | 0.000 | 0.001 | 0.000 | 16.714 | 8.998 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 2.116 | 0.020 | 0.000 | 0.002 | 1 | 5 | 0.882 | 0.876 | 0.143 | 0.004 | 14.834 | 0.392 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 1.000 | 0.000 | 0.001 | 0.000 | 4.224 | 0.853 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 1.182 | 0.031 | 0.000 | 0.001 | 1 | 1 | 0.844 | 0.854 | 0.086 | 0.001 | 13.670 | 0.396 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 1.000 | 0.000 | 0.001 | 0.000 | 3.272 | 0.813 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.688 | 0.0 | 0.697 | 0.0 | random | NaN | 30 | NaN | 0.347 | 0.0 | 1.986 | 0.0 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.683 | 0.0 | 0.703 | 0.0 | k-means++ | NaN | 30 | NaN | 0.304 | 0.0 | 2.247 | 0.0 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.837 | 0.0 | 3.063 | 0.0 | random | NaN | 30 | NaN | 4.161 | 0.0 | 1.883 | 0.0 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.706 | 0.0 | 3.114 | 0.0 | k-means++ | NaN | 30 | NaN | 3.979 | 0.0 | 1.937 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.272 | 0.000 | random | 0.000 | 30 | 0.000 | 0.0 | 0.0 | 7.642 | 2.856 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.003 | 0.003 | 0.000 | 0.003 | random | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 14.104 | 15.770 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.288 | 0.000 | k-means++ | 0.001 | 30 | -0.000 | 0.0 | 0.0 | 7.554 | 3.316 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 9.548 | 4.654 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 12.381 | 0.000 | random | 0.001 | 30 | 0.002 | 0.0 | 0.0 | 6.532 | 2.297 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.015 | 0.002 | random | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 9.468 | 3.949 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 12.107 | 0.000 | k-means++ | 0.003 | 30 | 0.002 | 0.0 | 0.0 | 6.762 | 2.350 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.015 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 6.268 | 5.218 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.295 | 0.0 | 0.011 | 0.0 | k-means++ | NaN | 20 | NaN | 0.050 | 0.0 | 5.922 | 0.0 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.096 | 0.0 | 0.033 | 0.0 | random | NaN | 20 | NaN | 0.134 | 0.0 | 0.713 | 0.0 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 1.142 | 0.0 | 0.140 | 0.0 | k-means++ | NaN | 20 | NaN | 0.251 | 0.0 | 4.551 | 0.0 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.336 | 0.0 | 0.476 | 0.0 | random | NaN | 20 | NaN | 0.626 | 0.0 | 0.538 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.139 | 0.000 | k-means++ | -0.001 | 20 | 0.002 | 0.001 | 0.0 | 2.582 | 0.939 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.000 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.781 | 2.947 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.148 | 0.000 | random | 0.001 | 20 | 0.000 | 0.001 | 0.0 | 3.242 | 0.827 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.001 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 11.285 | 6.347 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 4.771 | 0.000 | k-means++ | 0.290 | 20 | 0.316 | 0.001 | 0.0 | 2.296 | 0.292 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.008 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.420 | 3.887 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 4.855 | 0.000 | random | 0.344 | 20 | 0.276 | 0.001 | 0.0 | 2.263 | 0.237 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.008 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.898 | 3.773 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 16.362 | 0.0 | [-0.0721086] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.865 | 0.0 | 5.710 | 0.0 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 1.279 | 0.0 | [1.62632056] | 0.001 | NaN | NaN | NaN | NaN | NaN | 1.135 | 0.0 | 1.127 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [46.8241519] | 0.0 | NaN | NaN | NaN | NaN | 0.535 | 0.000 | 0.0 | 0.804 | 0.309 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.17838488] | 0.0 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.334 | 0.241 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [88.55720054] | 0.0 | NaN | NaN | NaN | NaN | 0.290 | 0.004 | 0.0 | 0.524 | 0.089 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [15.93676781] | 0.0 | NaN | NaN | NaN | NaN | 1.000 | 0.001 | 0.0 | 0.153 | 0.083 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.291 | 0.0 | 0.275 | 0.0 | NaN | NaN | NaN | 0.298 | 0.0 | 0.977 | 0.0 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.614 | 0.0 | 0.496 | 0.0 | NaN | NaN | NaN | 0.365 | 0.0 | 4.418 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.012 | 0.001 | 6.495 | 0.0 | NaN | NaN | 0.114 | 0.023 | 0.003 | 0.547 | 0.071 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 0.707 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.604 | 0.375 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 4.395 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.638 | 0.360 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.007 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.814 | 0.891 | See | See |